A Robust Image Watermarking Scheme Based on SVD in the Spatial Domain

Heng Zhang, Chengyou Wang and Xiao Zhou
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Heng Zhang: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
Chengyou Wang: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
Xiao Zhou: School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China

Future Internet, 2017, vol. 9, issue 3, 1-16

Abstract: With the development of image processing technology, the copyright protection of digital images has become an urgent problem to be solved. As an effective method, the robust digital watermarking technique emerges at a historic moment. Currently, most robust watermarking schemes are performed in the transform domains, such as the discrete cosine transform (DCT) and singular value decomposition (SVD). Compared with spatial domain watermarking schemes, these methods have achieved good performance, such as better robustness and higher security. However, the computational complexity increases with the use of forward and reverse transforms. In this paper, we analyze the SVD-based watermarking scheme and its impact on the spatial domain. Based on this analysis and the mathematical characteristics of SVD, we present a robust image watermarking scheme where a binary watermark is embedded into the largest singular value of each image block in the spatial domain. Several experiments are conducted to verify the performance of the proposed watermarking scheme. The experimental results show that compared with the existing SVD domain watermarking schemes, our proposed method has maintained good robustness against various attacks. Moreover, it avoids the false positive problem existing in traditional SVD-based watermarking schemes and has lower computational complexity.

Keywords: robust image watermarking; singular value decomposition (SVD); spatial domain; Arnold transform (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2017
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